import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path

from requests.exceptions import HTTPError

from transformers.utils import (
    CONFIG_NAME,
    FLAX_WEIGHTS_NAME,
    TF2_WEIGHTS_NAME,
    TRANSFORMERS_CACHE,
    WEIGHTS_NAME,
    cached_file,
    get_file_from_repo,
    has_file,
)


__a    :	str         =    """hf-internal-testing/tiny-random-bert"""
__a    :	Optional[Any]         =    os.path.join(TRANSFORMERS_CACHE, """models--hf-internal-testing--tiny-random-bert""")
__a    :	str         =    """9b8c223d42b2188cb49d29af482996f9d0f3e5a6"""




class   _UpperCamelCase (		unittest.TestCase					):


			"""simple docstring"""






			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							__lowercase					     =				cached_file(lowerCAmelCase__ ,  lowerCAmelCase__		)
							# Should have downloaded the file in here
							self.assertTrue(os.path.isdir(lowerCAmelCase__		)		)
							# Cache should contain at least those three subfolders:
							for subfolder in ["blobs", "refs", "snapshots"]:
											self.assertTrue(os.path.isdir(os.path.join(lowerCAmelCase__ ,  lowerCAmelCase__		)		)		)
							with open(os.path.join(lowerCAmelCase__ ,  '''refs''' ,  '''main'''		)		) as f:
											__lowercase					     =				f.read()
							self.assertEqual(lowerCAmelCase__ ,  os.path.join(lowerCAmelCase__ ,  '''snapshots''' ,  lowerCAmelCase__ ,  lowerCAmelCase__		)		)
							self.assertTrue(os.path.isfile(lowerCAmelCase__		)		)

							# File is cached at the same place the second time.
							__lowercase					     =				cached_file(lowerCAmelCase__ ,  lowerCAmelCase__		)
							self.assertEqual(lowerCAmelCase__ ,  lowerCAmelCase__		)

							# Using a specific revision to test the full commit hash.
							__lowercase					     =				cached_file(lowerCAmelCase__ ,  lowerCAmelCase__ ,  revision='''9b8c223'''		)
							self.assertEqual(lowerCAmelCase__ ,  os.path.join(lowerCAmelCase__ ,  '''snapshots''' ,  lowerCAmelCase__ ,  lowerCAmelCase__		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   List[str]:

							'''simple docstring'''


							with self.assertRaisesRegex(lowerCAmelCase__ ,  '''is not a valid model identifier'''		):
											__lowercase					     =				cached_file('''tiny-random-bert''' ,  lowerCAmelCase__		)

							with self.assertRaisesRegex(lowerCAmelCase__ ,  '''is not a valid git identifier'''		):
											__lowercase					     =				cached_file(lowerCAmelCase__ ,  lowerCAmelCase__ ,  revision='''aaaa'''		)

							with self.assertRaisesRegex(lowerCAmelCase__ ,  '''does not appear to have a file named'''		):
											__lowercase					     =				cached_file(lowerCAmelCase__ ,  '''conf'''		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							with self.assertRaisesRegex(lowerCAmelCase__ ,  '''does not appear to have a file named'''		):
											__lowercase					     =				cached_file(lowerCAmelCase__ ,  '''conf'''		)

							with open(os.path.join(lowerCAmelCase__ ,  '''refs''' ,  '''main'''		)		) as f:
											__lowercase					     =				f.read()
							self.assertTrue(os.path.isfile(os.path.join(lowerCAmelCase__ ,  '''.no_exist''' ,  lowerCAmelCase__ ,  '''conf'''		)		)		)

							__lowercase					     =				cached_file(lowerCAmelCase__ ,  '''conf''' ,  _raise_exceptions_for_missing_entries=lowerCAmelCase__		)
							self.assertIsNone(lowerCAmelCase__		)

							__lowercase					     =				cached_file(lowerCAmelCase__ ,  '''conf''' ,  local_files_only=lowerCAmelCase__ ,  _raise_exceptions_for_missing_entries=lowerCAmelCase__		)
							self.assertIsNone(lowerCAmelCase__		)

							__lowercase					     =				mock.Mock()
							__lowercase					     =				5_00
							__lowercase					     =				{}
							__lowercase					     =				HTTPError
							__lowercase					     =				{}

							# Under the mock environment we get a 500 error when trying to reach the tokenizer.
							with mock.patch('''requests.Session.request''' ,  return_value=lowerCAmelCase__		) as mock_head:
											__lowercase					     =				cached_file(lowerCAmelCase__ ,  '''conf''' ,  _raise_exceptions_for_connection_errors=lowerCAmelCase__		)
											self.assertIsNone(lowerCAmelCase__		)
											# This check we did call the fake head request
											mock_head.assert_called()





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[Any]:

							'''simple docstring'''


							self.assertTrue(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,  lowerCAmelCase__		)		)
							self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,  lowerCAmelCase__		)		)
							self.assertFalse(has_file('''hf-internal-testing/tiny-bert-pt-only''' ,  lowerCAmelCase__		)		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Optional[int]:

							'''simple docstring'''


							self.assertIsNone(get_file_from_repo('''bert-base-cased''' ,  '''ahah.txt'''		)		)

							# The function raises if the repository does not exist.
							with self.assertRaisesRegex(lowerCAmelCase__ ,  '''is not a valid model identifier'''		):
											get_file_from_repo('''bert-base-case''' ,  lowerCAmelCase__		)

							# The function raises if the revision does not exist.
							with self.assertRaisesRegex(lowerCAmelCase__ ,  '''is not a valid git identifier'''		):
											get_file_from_repo('''bert-base-cased''' ,  lowerCAmelCase__ ,  revision='''ahaha'''		)

							__lowercase					     =				get_file_from_repo('''bert-base-cased''' ,  lowerCAmelCase__		)
							# The name is the cached name which is not very easy to test, so instead we load the content.
							__lowercase					     =				json.loads(open(lowerCAmelCase__ ,  '''r'''		).read()		)
							self.assertEqual(config['''hidden_size'''] ,  7_68		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Union[str, Any]:

							'''simple docstring'''


							with tempfile.TemporaryDirectory() as tmp_dir:
											__lowercase					     =				Path(lowerCAmelCase__		) / '''a.txt'''
											filename.touch()
											self.assertEqual(get_file_from_repo(lowerCAmelCase__ ,  '''a.txt'''		) ,  str(lowerCAmelCase__		)		)

											self.assertIsNone(get_file_from_repo(lowerCAmelCase__ ,  '''b.txt'''		)		)
import argparse
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing how to properly calculate the metrics on the
# validation dataset when in a distributed system, and builds off the
# `nlp_example.py` script.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


__a    :	Union[str, Any]         =    1_6
__a    :	int         =    3_2





def 							UpperCAmelCase		(       lowercase				,     lowercase = 16				):







				"""simple docstring"""



				__lowercase					     =				AutoTokenizer.from_pretrained('''bert-base-cased'''				)
				__lowercase					     =				load_dataset('''glue'''				,     '''mrpc'''				)

				def tokenize_function(lowercase				):
								# max_length=None => use the model max length (it's actually the default)
								__lowercase					     =				tokenizer(examples['''sentence1''']				,     examples['''sentence2''']				,     truncation=lowercase				,     max_length=lowercase				)
								return outputs

				# Apply the method we just defined to all the examples in all the splits of the dataset
				# starting with the main process first:
				with accelerator.main_process_first():
								__lowercase					     =				datasets.map(
								    lowercase				,     batched=lowercase				,     remove_columns=['''idx''', '''sentence1''', '''sentence2''']				,     )

				# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
				# transformers library
				__lowercase					     =				tokenized_datasets.rename_column('''label'''				,     '''labels'''				)

				def collate_fn(lowercase				):
								# On TPU it's best to pad everything to the same length or training will be very slow.
								__lowercase					     =				128 if accelerator.distributed_type == DistributedType.TPU else None
								# When using mixed precision we want round multiples of 8/16
								if accelerator.mixed_precision == "fp8":
												__lowercase					     =				16
								elif accelerator.mixed_precision != "no":
												__lowercase					     =				8
								else:
												__lowercase					     =				None

								return tokenizer.pad(
								    lowercase				,     padding='''longest'''				,     max_length=lowercase				,     pad_to_multiple_of=lowercase				,     return_tensors='''pt'''				,     )

				# Instantiate dataloaders.
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''train''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)
				__lowercase					     =				DataLoader(
				    tokenized_datasets['''validation''']				,     shuffle=lowercase				,     collate_fn=lowercase				,     batch_size=lowercase				)

				return train_dataloader, eval_dataloader


# For testing only
if os.environ.get("""TESTING_MOCKED_DATALOADERS""", None) == "1":
		from accelerate.test_utils.training import mocked_dataloaders

		__a    :	List[Any]         =    mocked_dataloaders  # noqa: F811





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if os.environ.get('''TESTING_MOCKED_DATALOADERS'''				,     lowercase				) == "1":
								__lowercase					     =				2
				# Initialize accelerator
				__lowercase					     =				Accelerator(cpu=args.cpu				,     mixed_precision=args.mixed_precision				)
				# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
				__lowercase					     =				config['''lr''']
				__lowercase					     =				int(config['''num_epochs''']				)
				__lowercase					     =				int(config['''seed''']				)
				__lowercase					     =				int(config['''batch_size''']				)

				__lowercase					     =				evaluate.load('''glue'''				,     '''mrpc'''				)

				# If the batch size is too big we use gradient accumulation
				__lowercase					     =				1
				if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
								__lowercase					     =				batch_size // MAX_GPU_BATCH_SIZE
								__lowercase					     =				MAX_GPU_BATCH_SIZE

				set_seed(lowercase				)
				__lowercase     ,	__lowercase					     =				get_dataloaders(lowercase				,     lowercase				)
				# Instantiate the model (we build the model here so that the seed also control new weights initialization)
				__lowercase					     =				AutoModelForSequenceClassification.from_pretrained('''bert-base-cased'''				,     return_dict=lowercase				)

				# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
				# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
				# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
				__lowercase					     =				model.to(accelerator.device				)

				# Instantiate optimizer
				__lowercase					     =				AdamW(params=model.parameters()				,     lr=lowercase				)

				# Instantiate scheduler
				__lowercase					     =				get_linear_schedule_with_warmup(
				    optimizer=lowercase				,     num_warmup_steps=100				,     num_training_steps=(len(lowercase				) * num_epochs) // gradient_accumulation_steps				,     )

				# Prepare everything
				# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
				# prepare method.
				__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase     ,	__lowercase					     =				accelerator.prepare(
				    lowercase				,     lowercase				,     lowercase				,     lowercase				,     lowercase				)

				# Now we train the model
				for epoch in range(lowercase				):
								model.train()
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.loss
												__lowercase					     =				loss / gradient_accumulation_steps
												accelerator.backward(lowercase				)
												if step % gradient_accumulation_steps == 0:
																optimizer.step()
																lr_scheduler.step()
																optimizer.zero_grad()

								model.eval()
								__lowercase					     =				0
								for step, batch in enumerate(lowercase				):
												# We could avoid this line since we set the accelerator with `device_placement=True`.
												batch.to(accelerator.device				)
												with torch.no_grad():
																__lowercase					     =				model(**lowercase				)
												__lowercase					     =				outputs.logits.argmax(dim=-1				)
												__lowercase     ,	__lowercase					     =				accelerator.gather((predictions, batch['''labels'''])				)
												# New Code #
												# First we check if it's a distributed system
												if accelerator.use_distributed:
																# Then see if we're on the last batch of our eval dataloader
																if step == len(lowercase				) - 1:
																				# Last batch needs to be truncated on distributed systems as it contains additional samples
																				__lowercase					     =				predictions[: len(eval_dataloader.dataset				) - samples_seen]
																				__lowercase					     =				references[: len(eval_dataloader.dataset				) - samples_seen]
																else:
																				# Otherwise we add the number of samples seen
																				samples_seen += references.shape[0]
            # All of this can be avoided if you use `Accelerator.gather_for_metrics` instead of `Accelerator.gather`:
            # accelerator.gather_for_metrics((predictions, batch["labels"]))
												metric.add_batch(
												    predictions=lowercase				,     references=lowercase				,     )

								__lowercase					     =				metric.compute()
								# Use accelerator.print to print only on the main process.
								accelerator.print(F"epoch {epoch}:"				,     lowercase				)





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				argparse.ArgumentParser(description='''Simple example of training script.'''				)
				parser.add_argument(
				    '''--mixed_precision'''				,     type=lowercase				,     default=lowercase				,     choices=['''no''', '''fp16''', '''bf16''', '''fp8''']				,     help='''Whether to use mixed precision. Choose'''
				    '''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
				    '''and an Nvidia Ampere GPU.'''				,     )
				parser.add_argument('''--cpu'''				,     action='''store_true'''				,     help='''If passed, will train on the CPU.'''				)
				__lowercase					     =				parser.parse_args()
				__lowercase					     =				{'''lr''': 2E-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
				training_function(lowercase				,     lowercase				)


if __name__ == "__main__":
		main()